Complex optimization for big computational and experimental neutron datasets
Abstract
Here, we present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. Finally, we use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, and refine first principles calculations to better describe the experimental data.
- Authors:
-
- Univ. of Tennessee, Chattanooga, TN (United States). Dept. of Mathematics
- (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science & Technology Division
- Duke Univ., Durham, NC (United States). Dept. of Mechanical Engineering and Materials Science
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Spallation Neutron Source (SNS); Energy Frontier Research Centers (EFRC) (United States). Solid-State Solar-Thermal Energy Conversion Center (S3TEC)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1354653
- Alternate Identifier(s):
- OSTI ID: 1331028
- Grant/Contract Number:
- AC05-00OR22725; SC0001299; FG02-09ER46577; SC0016166
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Nanotechnology
- Additional Journal Information:
- Journal Volume: 27; Journal Issue: 48; Journal ID: ISSN 0957-4484
- Publisher:
- IOP Publishing
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE; image processing; inelastic neutron scattering; hierarchical optimization; mathematical regularization
Citation Formats
Bao, Feng, Oak Ridge National Lab., Archibald, Richard, Niedziela, Jennifer, Bansal, Dipanshu, and Delaire, Olivier. Complex optimization for big computational and experimental neutron datasets. United States: N. p., 2016.
Web. doi:10.1088/0957-4484/27/48/484002.
Bao, Feng, Oak Ridge National Lab., Archibald, Richard, Niedziela, Jennifer, Bansal, Dipanshu, & Delaire, Olivier. Complex optimization for big computational and experimental neutron datasets. United States. https://doi.org/10.1088/0957-4484/27/48/484002
Bao, Feng, Oak Ridge National Lab., Archibald, Richard, Niedziela, Jennifer, Bansal, Dipanshu, and Delaire, Olivier. Mon .
"Complex optimization for big computational and experimental neutron datasets". United States. https://doi.org/10.1088/0957-4484/27/48/484002. https://www.osti.gov/servlets/purl/1354653.
@article{osti_1354653,
title = {Complex optimization for big computational and experimental neutron datasets},
author = {Bao, Feng and Oak Ridge National Lab. and Archibald, Richard and Niedziela, Jennifer and Bansal, Dipanshu and Delaire, Olivier},
abstractNote = {Here, we present a framework to use high performance computing to determine accurate solutions to the inverse optimization problem of big experimental data against computational models. We demonstrate how image processing, mathematical regularization, and hierarchical modeling can be used to solve complex optimization problems on big data. We also demonstrate how both model and data information can be used to further increase solution accuracy of optimization by providing confidence regions for the processing and regularization algorithms. Finally, we use the framework in conjunction with the software package SIMPHONIES to analyze results from neutron scattering experiments on silicon single crystals, and refine first principles calculations to better describe the experimental data.},
doi = {10.1088/0957-4484/27/48/484002},
journal = {Nanotechnology},
number = 48,
volume = 27,
place = {United States},
year = {Mon Nov 07 00:00:00 EST 2016},
month = {Mon Nov 07 00:00:00 EST 2016}
}
Web of Science
Figures / Tables:
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